Flow cytometry involves the analysis of optical signals produced by suspensions of particles or biological cells passing in a fluid stream through a focused beam of light. The optical signals, derived from radiated light, for example from emission of fluorescence or from light scatter, are converted into voltage-versus-time pulse waveforms through the operation of a detector, such as photodiode or photomultiplier detectors.
Flow cytometry allows simultaneous multi-parametric analysis of the physical and biological characteristics of up to thousands of particles per second. Flow cytometry is routinely used in basic research, to interrogate populations of biological cells that may show cell type or gene/protein expression heterogeneity, in the diagnosis of medical conditions and has many other applications in research and clinical practice. A common application is to physically sort particles based on their properties, so as to purify populations of interest, such as in fluorescence-activated cell sorting (FACS).
Despite significant advances in flow cytometry analysis, established procedures fail to make effective use of the huge amount of data obtained for any given sample, or for any given particle, being analyzed in the cytometric device. For example, typical flow cytometry techniques employ thresholds in order to reduce background noise from the analysis. This is commonly done by designating a parameter as the trigger and setting a level in that parameter as the threshold. Any pulse that fails to exceed the threshold level is ignored in all detectors; any pulse that surpasses the threshold level is processed by the electronics. Typically the pulse height and in some cases pulse width across a trigger window (pulse time) are recorded.
Analyses of this kind however fail to interrogate more complex aspects of the data obtained by the detector, for example the exact shape of the waveform is not interrogated in detail, thereby potential cell doublets, where two cells are fused together, may not be identified. Cells associated with debris are not differentiated, and potentially valuable information on cell shape is lost.
Some improvements have been made in this respect, such as in the methods described in EP 1865303 and WO 2016/022276. These methods enable the detection of characteristic parameters of the cells, or of multi-peak signals, based on waveform analysis. However, the detection and determination of such waveform characteristics is typically carried out based on the height and width of the waveform, with or without some approximation of waveform, thereby discarding detailed information on the particular shape of the waveform as detected.
Moreover it has been proposed in the prior art to use wavelet-denoising algorithms to smooth the raw stream of data in order to improve the identification of peaks. Denoising or smoothing algorithms based upon a wavelet transformation have been proposed e.g. in Evander et al. for a microfluidic impedance cytometer or in Jagtiani et al. for an improved analysis of Coulter counter signals (Evander at al., Lab Chip, vol. 13, p. 722, 2012, Jagtiani et al., Measurement Science and Technology, Vol. 19 p. 065102, 2008). A wavelet-based dynamic peak picking has also been described in Damm et al. to achieve a more accurate cell counting in an in vivo flow cell cytometer (Damm et al. 2nd international conference on biomedical engineering and informateics, BMEI 2009). While the smoothing and denoising of the raw data allows for an improved peak detection and cell counting, detailed information on the particular shapes of the peaks are discarded.
In light of the prior art, there remains a significant need to provide additional means for effective processing of flow cytometer data, achieving improvements in identifying and/or characterizing the properties of the particles subject to analysis.